Optimization of multi-robot sumo fight simulation by a genetic algorithm to identify dominant robot capabilities

dc.contributor.authorLehner, Joël Enrico
dc.contributor.authorDornberger, Rolf
dc.contributor.authorSimic, Radovan
dc.contributor.authorHanne, Thomas
dc.date.accessioned2024-04-24T06:26:35Z
dc.date.available2024-04-24T06:26:35Z
dc.date.issued2019
dc.description.abstractThis paper analyzes the multirobot sumo fight simulation. This simulation is based on a computational model of several sumo fighters, which physically interact while trying to move the opponent out of the arena (lost fight). The problem is optimized using a genetic algorithm (GA), where the capabilities of not only one particular robot but of all robots simultaneously are improved. In this particular problem setup, the problem definition changes depending on the optimization path, because all robots also get better, competing against each other. The influence of different operators of the GA is investigated and compared. This paper raises the questions, which genetically controlled capabilities (e.g. size, speed) are dominant over time and how they can be identified by a sensitivity analysis using a GA. The results shed light on which parameters are dominant. This experiment typically opens up interesting fields of further research, especially about how to address optimization problems, where the optimization process influences the search space and how to eliminate the factor of randomness.
dc.event2019 IEEE Congress on Evolutionary Computation (CEC 2019)
dc.event.end2019-06-13
dc.event.start2019-06-10
dc.identifier.doi10.1109/CEC.2019.8790367
dc.identifier.isbn978-1-7281-2153-6
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/42598
dc.language.isoen
dc.relation.ispartof2019 IEEE Congress on Evolutionary Computation (CEC 2019). Proceedings
dc.spatialWellington
dc.subject.ddc330 - Wirtschaft
dc.titleOptimization of multi-robot sumo fight simulation by a genetic algorithm to identify dominant robot capabilities
dc.type04B - Beitrag Konferenzschrift
dspace.entity.typePublication
fhnw.InventedHereYes
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Wirtschaftde_CH
fhnw.affiliation.institutInstitut für Wirtschaftsinformatikde_CH
fhnw.openAccessCategoryClosed
fhnw.pagination490-496
fhnw.publicationStatePublished
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relation.isAuthorOfPublication64196f63-c326-4e10-935d-6776cc91354c
relation.isAuthorOfPublication3c3a8426-e114-4279-82c3-8acfe15277c2
relation.isAuthorOfPublication35d8348b-4dae-448a-af2a-4c5a4504da04
relation.isAuthorOfPublication.latestForDiscovery35d8348b-4dae-448a-af2a-4c5a4504da04
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